Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding

Figure 1 Study flowchart illustrating patient selection and analytical methodology. Left panel shows the development and internal validation process (2010-2016), including patient screening, selection criteria, data preprocessing, model development, and validation steps. Right panel demonstrates the external validation process (2017-2018) using an independent prospective cohort. AEVB: Acute esophageal variceal bleeding; GLMs: Generalized linear models; GLMBoost: Generalized linear models with boosting; HRS: Hepatorenal syndrome; NB: Naive Bayes; RF: Random forest.